Applied artificial intelligence (AI) to green power technology /:
"The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fu...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Nova Science Publishers,
[2022]
|
| Schriftenreihe: | Computer science, technology and applications
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fuel cell systems, and other emerging green power technologies are covered. Also, challenges in distributed energy generating systems and other relevant issues are covered"-- |
| Beschreibung: | 1 online resource. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9798886973174 |
Internformat
MARC
| LEADER | 00000cam a22000008i 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBA-on1350632812 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 220929s2022 nyu ob 001 0 eng | ||
| 010 | |a 2022045196 | ||
| 040 | |a DLC |b eng |e rda |c DLC |d OCLCF |d N$T |d OCLCO |d TMA |d OCLCQ |d QGK | ||
| 020 | |a 9798886973174 |q (electronic bk.) | ||
| 020 | |z 9798886971316 | ||
| 035 | |a (OCoLC)1350632812 | ||
| 042 | |a pcc | ||
| 050 | 0 | 0 | |a TK1005 |
| 082 | 7 | |a 621.31/21028563 |2 23/eng/20221128 | |
| 049 | |a MAIN | ||
| 245 | 0 | 0 | |a Applied artificial intelligence (AI) to green power technology / |c Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India. |
| 263 | |a 2212 | ||
| 264 | 1 | |a New York : |b Nova Science Publishers, |c [2022] | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Computer science, technology and applications | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fuel cell systems, and other emerging green power technologies are covered. Also, challenges in distributed energy generating systems and other relevant issues are covered"-- |c Provided by publisher. | ||
| 588 | |a Description based on print version record and CIP data provided by publisher; resource not viewed. | ||
| 505 | 0 | |a Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Energy Management and Artificial Intelligence -- Abstract -- Introduction -- Energy Management -- Overview -- Objectives -- Energy Management Process -- Electric Grid and Energy Management -- Artificial Intelligence -- AI for Energy Management -- Conclusion -- References -- Biographical Sketches -- Chapter 2 -- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI -- Abstract -- Introduction -- Fuel Cell -- Artificial Intelligence Techniques -- Artificial Neural Networks -- Multi-Layer Perceptrons (MLPs) -- Radial Basis Functions (RBF) -- Fuzzy Logic -- AI Applications in Renewable Energy -- AI in Solar Energy -- AI in Wind Energy -- AI in Geothermal -- Challenges in AI Techniques for Green Energy -- Conclusion -- References -- Chapter 3 -- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms -- Abstract -- Introduction -- Literature Review -- Problem Structure -- Artificial Intelligence Techniques -- Gravitational Search Algorithm (GSA) -- Procedure to Be Followed for SEIG Operation -- Genetic Algorithm (GA) -- Steps to Be Followed for Genetic Algorithm in SEIG -- Result and Discussion -- Conclusion -- Appendix -- References -- Chapter 4 -- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement -- Abstract -- Introduction -- Description of Micro/Pico Hydropower Generation System -- Self-Excited Induction Generator: An Overview -- Problem Formulation -- Estimation of Hydro Capacity -- Application of AI for Performance Improvement -- Machine Learning -- Deep Learning -- Artificial Neural Network (ANN) -- Fuzzy Logic -- Adaptive Neuro-Fuzzy Interface System (ANFIS). | |
| 505 | 8 | |a Conclusion -- References -- Chapter 5 -- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems -- Abstract -- Introduction -- Basics of Solar Energy -- Solar Module Characteristics -- Maximum Power Point Tracking Techniques -- Perturb and Observe (P& -- O) Technique -- P& -- O Based Multiple Power Sample MPPT Technique -- Adaptive Perturb and Observe Technique -- Incremental Conductance Method -- Regulated Incremental and Conductance MPPT Technique -- Variable Step Incremental Conductance Technique -- Fractional Open Circuit Voltage Method (FOCV) -- Semi-Pilot Cell FOCV MPPT Technique -- Fractional Short Circuit Current (FSCC) Method -- Soft Computing Techniques -- Fuzzy Logic Control (FLC) -- Artificial Neural Network (ANN) Control -- Evolutionary Computing Control -- Comparison between Various MPPT Techniques -- Conclusion -- References -- Chapter 6 -- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems -- Abstract -- Introduction -- A Variety of Renewable Energy Sources -- Wind Power -- Solar Power -- Small Hydropower -- Biomass -- Geothermal -- Trends of RES around the Globe -- Solar Cell -- Operating Principle -- The Need of Renewable Energy -- The Mathematical Equation for MPP -- Literature Review -- Simulation Models and Blocks -- PV Modelling -- Photovoltaic Cell Simulink Model in MATLAB -- Effect of Load Mismatching -- Boost Converter -- Procedure for Designing a Boost Converter -- Maximum Power Point Tracking Algorithms -- A Study on MPPT Techniques -- Algorithm for Fuzzy Logic -- Detailed Information of Perturb and Observe Algorithm -- Implementation Method -- Result and Discussion -- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques -- Conclusion -- Future Scope -- References -- Chapter 7. | |
| 505 | 8 | |a Different Reconfiguration Approaches for Photovoltaic Systems -- Abstract -- Introduction -- Mathematical Modelling of Solar Cell -- Various Modelling Topologies for Observing PSC Effects -- Basic Connecting Topologies -- Series-Parallel (S-P) -- Bridge-Linked (B-L) -- Total Cross-Tied (TCT) -- Advanced Reconfiguration Topologies -- Ken-Ken Reconfiguration (K-K) -- Arithmetic Sequence Reconfiguration (AS) -- L-Shape Reconfiguration (L-S) -- Performance Indices under PSC -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Result and Discussion -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Comparison of TCT and L-S -- Conclusion -- References -- Chapter 8 -- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System -- Abstract -- Introduction -- System Description and Modeling -- Wind Turbine Model -- Maximum Power Point Tracking -- WTS Maximum Power Point Tracking Algorithms -- Grey Wolf Optimization Based MPPT Algorithm -- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT -- Whale Search Optimization Algorithm Based MPPT -- Differential Squirrel Search Algorithm Based MPPT -- Grasshopper Optimization Based MPPT -- Experimental Assesment -- Result and Discussion -- Conclusion -- References -- Chapter 9 -- Wind Power Prediction Using Hybrid Soft Computing Models -- Abstract -- Introduction -- Wind Power Prediction Techniques -- Wavelet Transform (WT) -- Adaptive Network-Based Fuzzy Inference System (ANFIS) -- Dynamic Recurrent Neural Networks (DNNs) -- NAR Neural Network -- NARX Neural Network -- Dynamic Particle Swarm Optimization (DPSO) -- Wind Power Forecasting Using the Proposed Hybrid Technique. | |
| 505 | 8 | |a Wind Power Prediction Using Hybrid NAR/NARX Model -- Conclusion -- References -- Chapter 10 -- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence -- Abstract -- Introduction -- Problem Formulation -- Design Problem -- Optimizing Techniques -- Algorithm of GSA and GSA-PSO Technique -- Result and Discussion -- Conclusion -- References -- Chapter 11 -- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability -- Abstract -- Introduction -- The Existing Indices for Assessment of Voltage Stability -- Line Stability Index (Lmn) -- Fast Voltage Stability Index (FVSI) -- New Voltage Stability Index (NVSI) -- Proposed Modified Voltage Stability Index (MVSI) -- Results and Comparative Analysis of MVSI vs Other Indices -- IEEE 30 Bus System Results -- Base Load Operating Condition -- Heavy Active Loading Condition -- Heavy Reactive Loading Condition -- Heavy MVA Loading Condition -- IEEE 57 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- IEEE 118 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- The Machine Learning Approach for Voltage Stability Assessment -- The Exponential GPR Machine Learning Algorithm -- Methodology -- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices -- Comparative Analysis -- IEEE 30 Bus System -- IEEE 57 Bus System -- IEEE 118 Bus System -- Conclusion -- References -- Biographical Sketches -- Editors' Contact Information -- Index -- Blank Page. | |
| 650 | 0 | |a Electric power production |x Energy conservation. | |
| 650 | 0 | |a Clean energy |x Data processing. | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 6 | |a Électricité |x Production |x Économies d'énergie. | |
| 650 | 6 | |a Énergies propres |x Informatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 700 | 1 | |a Kumar Chauhan, Yogesh, |e editor. | |
| 700 | 1 | |a Kumar Behera, Ranjan, |e editor. | |
| 700 | 1 | |a Singh, Asheesh K., |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Applied artificial intelligence (AI) to green power technology |d New York : Nova Science Publishers, [2022] |z 9798886971316 |w (DLC) 2022045195 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3386455 |3 Volltext |
| 938 | |a YBP Library Services |b YANK |n 18207514 | ||
| 938 | |a EBSCOhost |b EBSC |n 3386455 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBA | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1350632812 |
|---|---|
| _version_ | 1816882566234374144 |
| adam_text | |
| any_adam_object | |
| author2 | Kumar Chauhan, Yogesh Kumar Behera, Ranjan Singh, Asheesh K. |
| author2_role | edt edt edt |
| author2_variant | c y k cy cyk b r k br brk a k s ak aks |
| author_facet | Kumar Chauhan, Yogesh Kumar Behera, Ranjan Singh, Asheesh K. |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | T - Technology |
| callnumber-label | TK1005 |
| callnumber-raw | TK1005 |
| callnumber-search | TK1005 |
| callnumber-sort | TK 41005 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| collection | ZDB-4-EBA |
| contents | Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Energy Management and Artificial Intelligence -- Abstract -- Introduction -- Energy Management -- Overview -- Objectives -- Energy Management Process -- Electric Grid and Energy Management -- Artificial Intelligence -- AI for Energy Management -- Conclusion -- References -- Biographical Sketches -- Chapter 2 -- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI -- Abstract -- Introduction -- Fuel Cell -- Artificial Intelligence Techniques -- Artificial Neural Networks -- Multi-Layer Perceptrons (MLPs) -- Radial Basis Functions (RBF) -- Fuzzy Logic -- AI Applications in Renewable Energy -- AI in Solar Energy -- AI in Wind Energy -- AI in Geothermal -- Challenges in AI Techniques for Green Energy -- Conclusion -- References -- Chapter 3 -- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms -- Abstract -- Introduction -- Literature Review -- Problem Structure -- Artificial Intelligence Techniques -- Gravitational Search Algorithm (GSA) -- Procedure to Be Followed for SEIG Operation -- Genetic Algorithm (GA) -- Steps to Be Followed for Genetic Algorithm in SEIG -- Result and Discussion -- Conclusion -- Appendix -- References -- Chapter 4 -- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement -- Abstract -- Introduction -- Description of Micro/Pico Hydropower Generation System -- Self-Excited Induction Generator: An Overview -- Problem Formulation -- Estimation of Hydro Capacity -- Application of AI for Performance Improvement -- Machine Learning -- Deep Learning -- Artificial Neural Network (ANN) -- Fuzzy Logic -- Adaptive Neuro-Fuzzy Interface System (ANFIS). Conclusion -- References -- Chapter 5 -- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems -- Abstract -- Introduction -- Basics of Solar Energy -- Solar Module Characteristics -- Maximum Power Point Tracking Techniques -- Perturb and Observe (P& -- O) Technique -- P& -- O Based Multiple Power Sample MPPT Technique -- Adaptive Perturb and Observe Technique -- Incremental Conductance Method -- Regulated Incremental and Conductance MPPT Technique -- Variable Step Incremental Conductance Technique -- Fractional Open Circuit Voltage Method (FOCV) -- Semi-Pilot Cell FOCV MPPT Technique -- Fractional Short Circuit Current (FSCC) Method -- Soft Computing Techniques -- Fuzzy Logic Control (FLC) -- Artificial Neural Network (ANN) Control -- Evolutionary Computing Control -- Comparison between Various MPPT Techniques -- Conclusion -- References -- Chapter 6 -- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems -- Abstract -- Introduction -- A Variety of Renewable Energy Sources -- Wind Power -- Solar Power -- Small Hydropower -- Biomass -- Geothermal -- Trends of RES around the Globe -- Solar Cell -- Operating Principle -- The Need of Renewable Energy -- The Mathematical Equation for MPP -- Literature Review -- Simulation Models and Blocks -- PV Modelling -- Photovoltaic Cell Simulink Model in MATLAB -- Effect of Load Mismatching -- Boost Converter -- Procedure for Designing a Boost Converter -- Maximum Power Point Tracking Algorithms -- A Study on MPPT Techniques -- Algorithm for Fuzzy Logic -- Detailed Information of Perturb and Observe Algorithm -- Implementation Method -- Result and Discussion -- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques -- Conclusion -- Future Scope -- References -- Chapter 7. Different Reconfiguration Approaches for Photovoltaic Systems -- Abstract -- Introduction -- Mathematical Modelling of Solar Cell -- Various Modelling Topologies for Observing PSC Effects -- Basic Connecting Topologies -- Series-Parallel (S-P) -- Bridge-Linked (B-L) -- Total Cross-Tied (TCT) -- Advanced Reconfiguration Topologies -- Ken-Ken Reconfiguration (K-K) -- Arithmetic Sequence Reconfiguration (AS) -- L-Shape Reconfiguration (L-S) -- Performance Indices under PSC -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Result and Discussion -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Comparison of TCT and L-S -- Conclusion -- References -- Chapter 8 -- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System -- Abstract -- Introduction -- System Description and Modeling -- Wind Turbine Model -- Maximum Power Point Tracking -- WTS Maximum Power Point Tracking Algorithms -- Grey Wolf Optimization Based MPPT Algorithm -- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT -- Whale Search Optimization Algorithm Based MPPT -- Differential Squirrel Search Algorithm Based MPPT -- Grasshopper Optimization Based MPPT -- Experimental Assesment -- Result and Discussion -- Conclusion -- References -- Chapter 9 -- Wind Power Prediction Using Hybrid Soft Computing Models -- Abstract -- Introduction -- Wind Power Prediction Techniques -- Wavelet Transform (WT) -- Adaptive Network-Based Fuzzy Inference System (ANFIS) -- Dynamic Recurrent Neural Networks (DNNs) -- NAR Neural Network -- NARX Neural Network -- Dynamic Particle Swarm Optimization (DPSO) -- Wind Power Forecasting Using the Proposed Hybrid Technique. Wind Power Prediction Using Hybrid NAR/NARX Model -- Conclusion -- References -- Chapter 10 -- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence -- Abstract -- Introduction -- Problem Formulation -- Design Problem -- Optimizing Techniques -- Algorithm of GSA and GSA-PSO Technique -- Result and Discussion -- Conclusion -- References -- Chapter 11 -- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability -- Abstract -- Introduction -- The Existing Indices for Assessment of Voltage Stability -- Line Stability Index (Lmn) -- Fast Voltage Stability Index (FVSI) -- New Voltage Stability Index (NVSI) -- Proposed Modified Voltage Stability Index (MVSI) -- Results and Comparative Analysis of MVSI vs Other Indices -- IEEE 30 Bus System Results -- Base Load Operating Condition -- Heavy Active Loading Condition -- Heavy Reactive Loading Condition -- Heavy MVA Loading Condition -- IEEE 57 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- IEEE 118 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- The Machine Learning Approach for Voltage Stability Assessment -- The Exponential GPR Machine Learning Algorithm -- Methodology -- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices -- Comparative Analysis -- IEEE 30 Bus System -- IEEE 57 Bus System -- IEEE 118 Bus System -- Conclusion -- References -- Biographical Sketches -- Editors' Contact Information -- Index -- Blank Page. |
| ctrlnum | (OCoLC)1350632812 |
| dewey-full | 621.31/21028563 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.31/21028563 |
| dewey-search | 621.31/21028563 |
| dewey-sort | 3621.31 821028563 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>10262cam a22005778i 4500</leader><controlfield tag="001">ZDB-4-EBA-on1350632812</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr |||||||||||</controlfield><controlfield tag="008">220929s2022 nyu ob 001 0 eng </controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a"> 2022045196</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DLC</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="c">DLC</subfield><subfield code="d">OCLCF</subfield><subfield code="d">N$T</subfield><subfield code="d">OCLCO</subfield><subfield code="d">TMA</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">QGK</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9798886973174</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9798886971316</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1350632812</subfield></datafield><datafield tag="042" ind1=" " ind2=" "><subfield code="a">pcc</subfield></datafield><datafield tag="050" ind1="0" ind2="0"><subfield code="a">TK1005</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">621.31/21028563</subfield><subfield code="2">23/eng/20221128</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">Applied artificial intelligence (AI) to green power technology /</subfield><subfield code="c">Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India.</subfield></datafield><datafield tag="263" ind1=" " ind2=" "><subfield code="a">2212</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York :</subfield><subfield code="b">Nova Science Publishers,</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Computer science, technology and applications</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fuel cell systems, and other emerging green power technologies are covered. Also, challenges in distributed energy generating systems and other relevant issues are covered"--</subfield><subfield code="c">Provided by publisher.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on print version record and CIP data provided by publisher; resource not viewed.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Energy Management and Artificial Intelligence -- Abstract -- Introduction -- Energy Management -- Overview -- Objectives -- Energy Management Process -- Electric Grid and Energy Management -- Artificial Intelligence -- AI for Energy Management -- Conclusion -- References -- Biographical Sketches -- Chapter 2 -- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI -- Abstract -- Introduction -- Fuel Cell -- Artificial Intelligence Techniques -- Artificial Neural Networks -- Multi-Layer Perceptrons (MLPs) -- Radial Basis Functions (RBF) -- Fuzzy Logic -- AI Applications in Renewable Energy -- AI in Solar Energy -- AI in Wind Energy -- AI in Geothermal -- Challenges in AI Techniques for Green Energy -- Conclusion -- References -- Chapter 3 -- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms -- Abstract -- Introduction -- Literature Review -- Problem Structure -- Artificial Intelligence Techniques -- Gravitational Search Algorithm (GSA) -- Procedure to Be Followed for SEIG Operation -- Genetic Algorithm (GA) -- Steps to Be Followed for Genetic Algorithm in SEIG -- Result and Discussion -- Conclusion -- Appendix -- References -- Chapter 4 -- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement -- Abstract -- Introduction -- Description of Micro/Pico Hydropower Generation System -- Self-Excited Induction Generator: An Overview -- Problem Formulation -- Estimation of Hydro Capacity -- Application of AI for Performance Improvement -- Machine Learning -- Deep Learning -- Artificial Neural Network (ANN) -- Fuzzy Logic -- Adaptive Neuro-Fuzzy Interface System (ANFIS).</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Conclusion -- References -- Chapter 5 -- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems -- Abstract -- Introduction -- Basics of Solar Energy -- Solar Module Characteristics -- Maximum Power Point Tracking Techniques -- Perturb and Observe (P&amp -- O) Technique -- P&amp -- O Based Multiple Power Sample MPPT Technique -- Adaptive Perturb and Observe Technique -- Incremental Conductance Method -- Regulated Incremental and Conductance MPPT Technique -- Variable Step Incremental Conductance Technique -- Fractional Open Circuit Voltage Method (FOCV) -- Semi-Pilot Cell FOCV MPPT Technique -- Fractional Short Circuit Current (FSCC) Method -- Soft Computing Techniques -- Fuzzy Logic Control (FLC) -- Artificial Neural Network (ANN) Control -- Evolutionary Computing Control -- Comparison between Various MPPT Techniques -- Conclusion -- References -- Chapter 6 -- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems -- Abstract -- Introduction -- A Variety of Renewable Energy Sources -- Wind Power -- Solar Power -- Small Hydropower -- Biomass -- Geothermal -- Trends of RES around the Globe -- Solar Cell -- Operating Principle -- The Need of Renewable Energy -- The Mathematical Equation for MPP -- Literature Review -- Simulation Models and Blocks -- PV Modelling -- Photovoltaic Cell Simulink Model in MATLAB -- Effect of Load Mismatching -- Boost Converter -- Procedure for Designing a Boost Converter -- Maximum Power Point Tracking Algorithms -- A Study on MPPT Techniques -- Algorithm for Fuzzy Logic -- Detailed Information of Perturb and Observe Algorithm -- Implementation Method -- Result and Discussion -- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques -- Conclusion -- Future Scope -- References -- Chapter 7.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Different Reconfiguration Approaches for Photovoltaic Systems -- Abstract -- Introduction -- Mathematical Modelling of Solar Cell -- Various Modelling Topologies for Observing PSC Effects -- Basic Connecting Topologies -- Series-Parallel (S-P) -- Bridge-Linked (B-L) -- Total Cross-Tied (TCT) -- Advanced Reconfiguration Topologies -- Ken-Ken Reconfiguration (K-K) -- Arithmetic Sequence Reconfiguration (AS) -- L-Shape Reconfiguration (L-S) -- Performance Indices under PSC -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Result and Discussion -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Comparison of TCT and L-S -- Conclusion -- References -- Chapter 8 -- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System -- Abstract -- Introduction -- System Description and Modeling -- Wind Turbine Model -- Maximum Power Point Tracking -- WTS Maximum Power Point Tracking Algorithms -- Grey Wolf Optimization Based MPPT Algorithm -- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT -- Whale Search Optimization Algorithm Based MPPT -- Differential Squirrel Search Algorithm Based MPPT -- Grasshopper Optimization Based MPPT -- Experimental Assesment -- Result and Discussion -- Conclusion -- References -- Chapter 9 -- Wind Power Prediction Using Hybrid Soft Computing Models -- Abstract -- Introduction -- Wind Power Prediction Techniques -- Wavelet Transform (WT) -- Adaptive Network-Based Fuzzy Inference System (ANFIS) -- Dynamic Recurrent Neural Networks (DNNs) -- NAR Neural Network -- NARX Neural Network -- Dynamic Particle Swarm Optimization (DPSO) -- Wind Power Forecasting Using the Proposed Hybrid Technique.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Wind Power Prediction Using Hybrid NAR/NARX Model -- Conclusion -- References -- Chapter 10 -- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence -- Abstract -- Introduction -- Problem Formulation -- Design Problem -- Optimizing Techniques -- Algorithm of GSA and GSA-PSO Technique -- Result and Discussion -- Conclusion -- References -- Chapter 11 -- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability -- Abstract -- Introduction -- The Existing Indices for Assessment of Voltage Stability -- Line Stability Index (Lmn) -- Fast Voltage Stability Index (FVSI) -- New Voltage Stability Index (NVSI) -- Proposed Modified Voltage Stability Index (MVSI) -- Results and Comparative Analysis of MVSI vs Other Indices -- IEEE 30 Bus System Results -- Base Load Operating Condition -- Heavy Active Loading Condition -- Heavy Reactive Loading Condition -- Heavy MVA Loading Condition -- IEEE 57 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- IEEE 118 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- The Machine Learning Approach for Voltage Stability Assessment -- The Exponential GPR Machine Learning Algorithm -- Methodology -- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices -- Comparative Analysis -- IEEE 30 Bus System -- IEEE 57 Bus System -- IEEE 118 Bus System -- Conclusion -- References -- Biographical Sketches -- Editors' Contact Information -- Index -- Blank Page.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Electric power production</subfield><subfield code="x">Energy conservation.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Clean energy</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85008180</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Électricité</subfield><subfield code="x">Production</subfield><subfield code="x">Économies d'énergie.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Énergies propres</subfield><subfield code="x">Informatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Intelligence artificielle.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">artificial intelligence.</subfield><subfield code="2">aat</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Artificial intelligence</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar Chauhan, Yogesh,</subfield><subfield code="e">editor.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar Behera, Ranjan,</subfield><subfield code="e">editor.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Asheesh K.,</subfield><subfield code="e">editor.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="t">Applied artificial intelligence (AI) to green power technology</subfield><subfield code="d">New York : Nova Science Publishers, [2022]</subfield><subfield code="z">9798886971316</subfield><subfield code="w">(DLC) 2022045195</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3386455</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">18207514</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">3386455</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-on1350632812 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:39Z |
| institution | BVB |
| isbn | 9798886973174 |
| language | English |
| lccn | 2022045196 |
| oclc_num | 1350632812 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource. |
| psigel | ZDB-4-EBA |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Nova Science Publishers, |
| record_format | marc |
| series2 | Computer science, technology and applications |
| spelling | Applied artificial intelligence (AI) to green power technology / Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India. 2212 New York : Nova Science Publishers, [2022] 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Computer science, technology and applications Includes bibliographical references and index. "The aim of this book is to explore the feasible solutions of various issues related to performance of green power technologies with the help of proven artificial intelligence techniques. Issues related to performance, wind energy conversion systems, micro/pico hydropower generation systems, fuel cell systems, and other emerging green power technologies are covered. Also, challenges in distributed energy generating systems and other relevant issues are covered"-- Provided by publisher. Description based on print version record and CIP data provided by publisher; resource not viewed. Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Energy Management and Artificial Intelligence -- Abstract -- Introduction -- Energy Management -- Overview -- Objectives -- Energy Management Process -- Electric Grid and Energy Management -- Artificial Intelligence -- AI for Energy Management -- Conclusion -- References -- Biographical Sketches -- Chapter 2 -- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI -- Abstract -- Introduction -- Fuel Cell -- Artificial Intelligence Techniques -- Artificial Neural Networks -- Multi-Layer Perceptrons (MLPs) -- Radial Basis Functions (RBF) -- Fuzzy Logic -- AI Applications in Renewable Energy -- AI in Solar Energy -- AI in Wind Energy -- AI in Geothermal -- Challenges in AI Techniques for Green Energy -- Conclusion -- References -- Chapter 3 -- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms -- Abstract -- Introduction -- Literature Review -- Problem Structure -- Artificial Intelligence Techniques -- Gravitational Search Algorithm (GSA) -- Procedure to Be Followed for SEIG Operation -- Genetic Algorithm (GA) -- Steps to Be Followed for Genetic Algorithm in SEIG -- Result and Discussion -- Conclusion -- Appendix -- References -- Chapter 4 -- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement -- Abstract -- Introduction -- Description of Micro/Pico Hydropower Generation System -- Self-Excited Induction Generator: An Overview -- Problem Formulation -- Estimation of Hydro Capacity -- Application of AI for Performance Improvement -- Machine Learning -- Deep Learning -- Artificial Neural Network (ANN) -- Fuzzy Logic -- Adaptive Neuro-Fuzzy Interface System (ANFIS). Conclusion -- References -- Chapter 5 -- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems -- Abstract -- Introduction -- Basics of Solar Energy -- Solar Module Characteristics -- Maximum Power Point Tracking Techniques -- Perturb and Observe (P& -- O) Technique -- P& -- O Based Multiple Power Sample MPPT Technique -- Adaptive Perturb and Observe Technique -- Incremental Conductance Method -- Regulated Incremental and Conductance MPPT Technique -- Variable Step Incremental Conductance Technique -- Fractional Open Circuit Voltage Method (FOCV) -- Semi-Pilot Cell FOCV MPPT Technique -- Fractional Short Circuit Current (FSCC) Method -- Soft Computing Techniques -- Fuzzy Logic Control (FLC) -- Artificial Neural Network (ANN) Control -- Evolutionary Computing Control -- Comparison between Various MPPT Techniques -- Conclusion -- References -- Chapter 6 -- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems -- Abstract -- Introduction -- A Variety of Renewable Energy Sources -- Wind Power -- Solar Power -- Small Hydropower -- Biomass -- Geothermal -- Trends of RES around the Globe -- Solar Cell -- Operating Principle -- The Need of Renewable Energy -- The Mathematical Equation for MPP -- Literature Review -- Simulation Models and Blocks -- PV Modelling -- Photovoltaic Cell Simulink Model in MATLAB -- Effect of Load Mismatching -- Boost Converter -- Procedure for Designing a Boost Converter -- Maximum Power Point Tracking Algorithms -- A Study on MPPT Techniques -- Algorithm for Fuzzy Logic -- Detailed Information of Perturb and Observe Algorithm -- Implementation Method -- Result and Discussion -- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques -- Conclusion -- Future Scope -- References -- Chapter 7. Different Reconfiguration Approaches for Photovoltaic Systems -- Abstract -- Introduction -- Mathematical Modelling of Solar Cell -- Various Modelling Topologies for Observing PSC Effects -- Basic Connecting Topologies -- Series-Parallel (S-P) -- Bridge-Linked (B-L) -- Total Cross-Tied (TCT) -- Advanced Reconfiguration Topologies -- Ken-Ken Reconfiguration (K-K) -- Arithmetic Sequence Reconfiguration (AS) -- L-Shape Reconfiguration (L-S) -- Performance Indices under PSC -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Result and Discussion -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Comparison of TCT and L-S -- Conclusion -- References -- Chapter 8 -- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System -- Abstract -- Introduction -- System Description and Modeling -- Wind Turbine Model -- Maximum Power Point Tracking -- WTS Maximum Power Point Tracking Algorithms -- Grey Wolf Optimization Based MPPT Algorithm -- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT -- Whale Search Optimization Algorithm Based MPPT -- Differential Squirrel Search Algorithm Based MPPT -- Grasshopper Optimization Based MPPT -- Experimental Assesment -- Result and Discussion -- Conclusion -- References -- Chapter 9 -- Wind Power Prediction Using Hybrid Soft Computing Models -- Abstract -- Introduction -- Wind Power Prediction Techniques -- Wavelet Transform (WT) -- Adaptive Network-Based Fuzzy Inference System (ANFIS) -- Dynamic Recurrent Neural Networks (DNNs) -- NAR Neural Network -- NARX Neural Network -- Dynamic Particle Swarm Optimization (DPSO) -- Wind Power Forecasting Using the Proposed Hybrid Technique. Wind Power Prediction Using Hybrid NAR/NARX Model -- Conclusion -- References -- Chapter 10 -- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence -- Abstract -- Introduction -- Problem Formulation -- Design Problem -- Optimizing Techniques -- Algorithm of GSA and GSA-PSO Technique -- Result and Discussion -- Conclusion -- References -- Chapter 11 -- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability -- Abstract -- Introduction -- The Existing Indices for Assessment of Voltage Stability -- Line Stability Index (Lmn) -- Fast Voltage Stability Index (FVSI) -- New Voltage Stability Index (NVSI) -- Proposed Modified Voltage Stability Index (MVSI) -- Results and Comparative Analysis of MVSI vs Other Indices -- IEEE 30 Bus System Results -- Base Load Operating Condition -- Heavy Active Loading Condition -- Heavy Reactive Loading Condition -- Heavy MVA Loading Condition -- IEEE 57 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- IEEE 118 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- The Machine Learning Approach for Voltage Stability Assessment -- The Exponential GPR Machine Learning Algorithm -- Methodology -- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices -- Comparative Analysis -- IEEE 30 Bus System -- IEEE 57 Bus System -- IEEE 118 Bus System -- Conclusion -- References -- Biographical Sketches -- Editors' Contact Information -- Index -- Blank Page. Electric power production Energy conservation. Clean energy Data processing. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Électricité Production Économies d'énergie. Énergies propres Informatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Kumar Chauhan, Yogesh, editor. Kumar Behera, Ranjan, editor. Singh, Asheesh K., editor. Print version: Applied artificial intelligence (AI) to green power technology New York : Nova Science Publishers, [2022] 9798886971316 (DLC) 2022045195 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3386455 Volltext |
| spellingShingle | Applied artificial intelligence (AI) to green power technology / Intro -- Contents -- Preface -- Acknowledgments -- Chapter 1 -- Energy Management and Artificial Intelligence -- Abstract -- Introduction -- Energy Management -- Overview -- Objectives -- Energy Management Process -- Electric Grid and Energy Management -- Artificial Intelligence -- AI for Energy Management -- Conclusion -- References -- Biographical Sketches -- Chapter 2 -- Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI -- Abstract -- Introduction -- Fuel Cell -- Artificial Intelligence Techniques -- Artificial Neural Networks -- Multi-Layer Perceptrons (MLPs) -- Radial Basis Functions (RBF) -- Fuzzy Logic -- AI Applications in Renewable Energy -- AI in Solar Energy -- AI in Wind Energy -- AI in Geothermal -- Challenges in AI Techniques for Green Energy -- Conclusion -- References -- Chapter 3 -- Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms -- Abstract -- Introduction -- Literature Review -- Problem Structure -- Artificial Intelligence Techniques -- Gravitational Search Algorithm (GSA) -- Procedure to Be Followed for SEIG Operation -- Genetic Algorithm (GA) -- Steps to Be Followed for Genetic Algorithm in SEIG -- Result and Discussion -- Conclusion -- Appendix -- References -- Chapter 4 -- Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement -- Abstract -- Introduction -- Description of Micro/Pico Hydropower Generation System -- Self-Excited Induction Generator: An Overview -- Problem Formulation -- Estimation of Hydro Capacity -- Application of AI for Performance Improvement -- Machine Learning -- Deep Learning -- Artificial Neural Network (ANN) -- Fuzzy Logic -- Adaptive Neuro-Fuzzy Interface System (ANFIS). Conclusion -- References -- Chapter 5 -- An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems -- Abstract -- Introduction -- Basics of Solar Energy -- Solar Module Characteristics -- Maximum Power Point Tracking Techniques -- Perturb and Observe (P& -- O) Technique -- P& -- O Based Multiple Power Sample MPPT Technique -- Adaptive Perturb and Observe Technique -- Incremental Conductance Method -- Regulated Incremental and Conductance MPPT Technique -- Variable Step Incremental Conductance Technique -- Fractional Open Circuit Voltage Method (FOCV) -- Semi-Pilot Cell FOCV MPPT Technique -- Fractional Short Circuit Current (FSCC) Method -- Soft Computing Techniques -- Fuzzy Logic Control (FLC) -- Artificial Neural Network (ANN) Control -- Evolutionary Computing Control -- Comparison between Various MPPT Techniques -- Conclusion -- References -- Chapter 6 -- Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems -- Abstract -- Introduction -- A Variety of Renewable Energy Sources -- Wind Power -- Solar Power -- Small Hydropower -- Biomass -- Geothermal -- Trends of RES around the Globe -- Solar Cell -- Operating Principle -- The Need of Renewable Energy -- The Mathematical Equation for MPP -- Literature Review -- Simulation Models and Blocks -- PV Modelling -- Photovoltaic Cell Simulink Model in MATLAB -- Effect of Load Mismatching -- Boost Converter -- Procedure for Designing a Boost Converter -- Maximum Power Point Tracking Algorithms -- A Study on MPPT Techniques -- Algorithm for Fuzzy Logic -- Detailed Information of Perturb and Observe Algorithm -- Implementation Method -- Result and Discussion -- Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques -- Conclusion -- Future Scope -- References -- Chapter 7. Different Reconfiguration Approaches for Photovoltaic Systems -- Abstract -- Introduction -- Mathematical Modelling of Solar Cell -- Various Modelling Topologies for Observing PSC Effects -- Basic Connecting Topologies -- Series-Parallel (S-P) -- Bridge-Linked (B-L) -- Total Cross-Tied (TCT) -- Advanced Reconfiguration Topologies -- Ken-Ken Reconfiguration (K-K) -- Arithmetic Sequence Reconfiguration (AS) -- L-Shape Reconfiguration (L-S) -- Performance Indices under PSC -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Result and Discussion -- Global Maximum Power Point (GMPP) -- Efficiency (Ƞ) -- Fill Factor (FF) -- % Power Loss (%PL) -- Mismatch Loss (ML) -- Execution Ratio (ER) -- Comparison of TCT and L-S -- Conclusion -- References -- Chapter 8 -- Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System -- Abstract -- Introduction -- System Description and Modeling -- Wind Turbine Model -- Maximum Power Point Tracking -- WTS Maximum Power Point Tracking Algorithms -- Grey Wolf Optimization Based MPPT Algorithm -- Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT -- Whale Search Optimization Algorithm Based MPPT -- Differential Squirrel Search Algorithm Based MPPT -- Grasshopper Optimization Based MPPT -- Experimental Assesment -- Result and Discussion -- Conclusion -- References -- Chapter 9 -- Wind Power Prediction Using Hybrid Soft Computing Models -- Abstract -- Introduction -- Wind Power Prediction Techniques -- Wavelet Transform (WT) -- Adaptive Network-Based Fuzzy Inference System (ANFIS) -- Dynamic Recurrent Neural Networks (DNNs) -- NAR Neural Network -- NARX Neural Network -- Dynamic Particle Swarm Optimization (DPSO) -- Wind Power Forecasting Using the Proposed Hybrid Technique. Wind Power Prediction Using Hybrid NAR/NARX Model -- Conclusion -- References -- Chapter 10 -- Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence -- Abstract -- Introduction -- Problem Formulation -- Design Problem -- Optimizing Techniques -- Algorithm of GSA and GSA-PSO Technique -- Result and Discussion -- Conclusion -- References -- Chapter 11 -- A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability -- Abstract -- Introduction -- The Existing Indices for Assessment of Voltage Stability -- Line Stability Index (Lmn) -- Fast Voltage Stability Index (FVSI) -- New Voltage Stability Index (NVSI) -- Proposed Modified Voltage Stability Index (MVSI) -- Results and Comparative Analysis of MVSI vs Other Indices -- IEEE 30 Bus System Results -- Base Load Operating Condition -- Heavy Active Loading Condition -- Heavy Reactive Loading Condition -- Heavy MVA Loading Condition -- IEEE 57 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- IEEE 118 Bus System Results -- Base Load Operating Condition -- Active Power Loading Condition -- Reactive Power Loading Condition -- The Machine Learning Approach for Voltage Stability Assessment -- The Exponential GPR Machine Learning Algorithm -- Methodology -- Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices -- Comparative Analysis -- IEEE 30 Bus System -- IEEE 57 Bus System -- IEEE 118 Bus System -- Conclusion -- References -- Biographical Sketches -- Editors' Contact Information -- Index -- Blank Page. Electric power production Energy conservation. Clean energy Data processing. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Électricité Production Économies d'énergie. Énergies propres Informatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 |
| title | Applied artificial intelligence (AI) to green power technology / |
| title_auth | Applied artificial intelligence (AI) to green power technology / |
| title_exact_search | Applied artificial intelligence (AI) to green power technology / |
| title_full | Applied artificial intelligence (AI) to green power technology / Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India. |
| title_fullStr | Applied artificial intelligence (AI) to green power technology / Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India. |
| title_full_unstemmed | Applied artificial intelligence (AI) to green power technology / Yogesh Kumar Chauhan, editor, Associate Professor, EED, KNIT, Uttar Pradesh, India, Ranjan Kumar Behera, editor, Department of Electrical Engineering, Indian Institute of Technology, Patna Bihta, Bihar, Inida, Asheesh K. Singh, editor, Professor, M. N. National Institute of Technology Allahabad, Prayagraj, India. |
| title_short | Applied artificial intelligence (AI) to green power technology / |
| title_sort | applied artificial intelligence ai to green power technology |
| topic | Electric power production Energy conservation. Clean energy Data processing. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Électricité Production Économies d'énergie. Énergies propres Informatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast |
| topic_facet | Electric power production Energy conservation. Clean energy Data processing. Artificial intelligence. Électricité Production Économies d'énergie. Énergies propres Informatique. Intelligence artificielle. artificial intelligence. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3386455 |
| work_keys_str_mv | AT kumarchauhanyogesh appliedartificialintelligenceaitogreenpowertechnology AT kumarbeheraranjan appliedartificialintelligenceaitogreenpowertechnology AT singhasheeshk appliedartificialintelligenceaitogreenpowertechnology |